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Article

Analysis of Differences in Wood Properties Among Four Poplar Species Under Different Site Conditions

by
Ruixia Qin
1,
Huadong Xu
1,*,
Yanbo Hu
2,*,
Peng Wang
1 and
Tianshu Zuo
1
1
Key Laboratory of Forest Sustainable Management and Environmental Microbial Engineering Laboratory of Heilongjiang Province, Northeast Forestry University, Harbin 150040, China
2
College of Life Sciences, Northeast Forestry University, Harbin 150040, China
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(11), 1745; https://doi.org/10.3390/f16111745
Submission received: 13 October 2025 / Revised: 14 November 2025 / Accepted: 17 November 2025 / Published: 19 November 2025
(This article belongs to the Section Wood Science and Forest Products)
Browse Figures
Versions Notes

Abstract

Through research on the effects of soil and meteorological factors on poplar wood properties, poplar clones with enhanced cold tolerance, drought resistance, and salt–alkali tolerance were selected for large-scale cultivation in the Western Songnen Plain, Northern China. We evaluated wood physical properties (basic density) and anatomical characteristics (annual ring width—RW, vessel number—CNO, vessel lumen area—LA) of 15-year-old Populus simonii × P. nigra, Populus alba × P. berolinensis, P. euramericana N3016 × P. ussuriensis, and Populus pseudo-cathayana × P. deltodides clones in the typical black soil area and saline–alkali land. The results showed that black soil region was more suitable for poplar growth, which was influenced by both soil and meteorological factors. Among soil factors, pH was the primary factor influencing the radial growth of poplar clones, exhibiting a negative correlation for all clones except P. alba × P. berolinensis. Furthermore, P. euramericana N3016 × P. ussuriensis was affected by organic carbon, while P. simonii × P. nigra and P. alba × P. berolinensis were more influenced by potassium. Among climatic factors, basic wood density, annual ring characteristics, and vessel structural parameters in all clones were primarily influenced by wind speed and sunshine, with air temperature having the least effect. Among the four clones, P. alba × P. berolinensis displayed better growth performance (higher RW) and basic wood density (0.29–0.41 g/cm3) at both sites, while P. simonii × P. nigra proved suitable for cold regions. Both clones showing dual adaptability to saline–alkali and black soil environments in Northeast China.

1. Introduction

In the context of global climate change, forests are facing increasingly severe environmental stresses such as drought, cold, and salinity [1,2,3]. Climate change (particularly warming, drought, sea-level rise, and extreme weather) alters regional water–heat and water balances, leading to the accumulation of soil salts in surface layers or their external intrusion. This exacerbates soil aridity and salinization, disrupting soil physical structure and biological activity [4,5,6], which not only weakens forest health and trees’ survival but also severely degrades or eliminates key ecosystem services such as carbon sequestration, soil and water conservation [7], and biodiversity habitats [8]. With the increasing frequency and intensity of environmental stresses, forest ecosystems are confronting escalating threats. Consequently, breeding or selecting species/genotypes with an enhanced resistance to salinity, cold, or drought has become an urgent priority for researchers and forestry practitioners. Meanwhile, a better understanding of environmental effects on wood properties can inform adjustments to integrated forest management strategies [9].
Currently, poplar trees rank among the world’s most widely distributed trees, predominantly occurring across temperate to cold regions. Characterized by rapid growth, early maturation, and strong adaptability, poplar tree species hold significant ecological and economic importance [10]. Global inventories document approximately 54.5 million hectares of natural poplar forests, with major stands located in China, Russia, Canada, and the United States. Commercial plantations cover an area of about 31.4 million hectares worldwide, predominantly concentrated in China and Canada [11]. Poplars are highly susceptible to environmental stresses, constraining their global forest land utilization and timber production [12,13]. Further, mixed poplar plantations generally yield good wood quality compared to natural poplar forests [14]. Nevertheless, poplar planting and management practices vary significantly across regions, resulting in substantial differences in land uses [15,16]. Different poplar cultivars possess distinct ecological requirements, with an optimal productivity achievable only when grown in suitable environmental conditions [17]. To optimize ecological resource utilization and improve plantation forest ecosystems, comprehensive investigations are required to elucidate how soil factors and meteorological conditions affect the growth dynamics of poplar plantations and wood physical–mechanical properties [18,19]. Wood anatomical characteristics (variation in vessel structure and ring structure) are important sources of information that determine the adaptability of trees to the ecological environment [20]. Under different environmental stresses (cold, drought, saline–alkali), the hydraulic compromise between transport efficiency and safety against embolism constitutes a critical determinant of poplar growth performance [21]. Xylem vessel anatomy—particularly the number of vessels and lumen area—serves as the structural foundation determining hydraulic efficiency and stress resistance [22,23]. Environmental factors such as soil nutrient availability, precipitation patterns, and temperature significantly influence wood formation processes through effects on vessel differentiation and cell wall deposition [24], thereby influencing critical wood quality indicators such as basic density and annual ring width. Basic wood density represents a fundamental parameter for wood quality assessment [25]. Numerous studies have identified soil factors (e.g., nitrogen) as regulators of poplar wood anatomy [26,27]. Many researchers have also focused on the effects of drought on poplar productivity and breeding poplar genotypes that combine high productivity with high water-use efficiency. Research on the effects of meteorological conditions on poplar wood properties remains limited, possibly due to the lagged impact of weather on wood formation [28]. However, studies on other tree species indicate that temperature and precipitation influence wood characteristics such as annual ring width and density [29,30,31].
The Songnen Plain in Northeast China serves as a critical cultivation base and representative distribution area for poplar industrial resources. This region is featured by three predominant soil types: sandy soil, saline–alkali soil, and black soil, with prominent environmental constraints, including an extremely low temperature, seasonal drought, windy erosion, and salinization [32,33,34]. Consequently, plant tolerances to cold, drought, and saline–alkali stresses consistently serve as the primary selection criteria for tree breeding programs in the region [35,36]. The P. simonii × P. nigra, developed through selection breeding in the 1960s, emerged as a main tree species in the area due to its adaptability to cold, drought, and saline–alkali conditions [37,38]. Pot experiments have demonstrated that new varieties such as P. × euramericana ‘Bofeng 3’, P. × euramericana ‘Bofeng 1’, P. × euramericana ‘Zhongxiong 1’, P. × euramericana ‘Zhongxiong 7’, and P. deltoides × P. suaveolens cl. ‘Zhongxiong 4’ exhibited good seedling growth performance, photosynthetic capacity, and water–nutrient-use efficiency compared to traditional varieties [39]. In recent decades, several improved cultivars including P. alba × P. Berolinensis (released in 1984), P. euramericana N3016 × P. ussuriensis (2013), and Populus Longfeng 1 (2018) have been developed in Northern China. However, the cultivation areas of these poplars remain limited. Driven by regional economic growth and timber market demands, the strategic cultivation of large-diameter, knot-free, good timber has become essential for upgrading and sustaining the poplar industry in the Songnen Plain [40]. Current research gaps include insufficient adaptability testing of target cultivars under typical site conditions in the Western Songnen Plain, particularly regarding wood property variations among middle-aged to near-mature poplar stands across different genotypes [41]. While numerous studies have examined soil impacts on dimensional growth parameters (height, DBH), few investigations address how soil–meteorological interactions influence the physical and anatomical characteristics of wood [42]. By elucidating the mechanisms through which environmental factors—particularly soil–climate interactions—influence poplar growth and wood formation and by evaluating and breeding superior poplar clones adapted to the harsh conditions of the Songnen Plain, we can provide scientific foundations and integrated management strategies for the sustainable development of the poplar industry and the strategic cultivation of high-quality large-diameter timber in this region and other areas with similar environmental conditions.
This study aimed to select optimized poplar cultivars with enhanced cold, drought, and saline–alkali tolerance for large-scale cultivation in the Western Songnen Plain by systematically evaluating the impacts of soil and meteorological factors on poplar growth and wood properties (basic wood density, ring width, vessel number, and average vessel lumen area). The investigation specifically focused on evaluating (1) the phenotypic variation in wood properties among different poplar cultivars under identical site conditions and (2) the environmental impacts on wood trait performances of identical cultivars across diverse locations. Through a comparative analysis of inter-cultivar performance and clone–environment interactions, this research identifies adaptive mechanisms of various poplar clones in diverse soil environments and provides critical data to address current challenges in plantation cultivation and management—particularly low site–clones compatibility. The findings establish a scientific foundation for implementing precision afforestation strategies in saline–alkali, cold, and arid regions.

2. Materials and Methods

2.1. Study Region and Sample Tree Selection

The study area was located in the Songnen Plain of Northeast China (121°37′−126°39′ N, 43°59′−48°33′ E, average elevation: 120–300 m), comprising two distinct ecological zones: the saline–alkali soils of Hongqi forest farm (124°13′ E, 45°45′ N) and the black soil region of Cuohai forest farm (122°51′ E, 47°27′ N). The primary tree species at Hongqi forest farm include poplar, sylvestris pine, larch, spruce, and ash, while the tree species at Cuohai forest farm are mainly larch, sylvestris pine, and poplar. Through AMMI (Additive Main Effects and Multiplicative Interaction) dual-marker analysis of tree height and stem diameter at breast height, it was revealed that site conditions are the primary factors driving growth variation among poplar clones. This approach successfully identified high-yielding and stable clones among 43 poplar clones. Optimal clones for different regions were selected via GGE (Genotype and Genotype × Environment) evaluation. Comprehensive comparisons led to the selection of four clones (Clone 5 (P. simonii × P. nigra), Clone 11 (P. euramericana N3016 × P. ussuriensis), Clone 20 (P. alba × P. berolinensis), and Clone 36 (P. pseudo-cathayana × P. deltodides)) which are characterized by high planting rates, phenotypic stability, and representative growth characteristics.
Dominant trees exhibiting optimal growth traits (straight boles, defect-free stems, and favorable crown development) were systematically selected from south-facing slopes with minimal terrain variation. Three representative individuals per poplar clone (mean DBH—approximately 20 cm measured at 1.3 m above ground level) were sampled for detailed analysis. Before felling, cardinal orientation (N-S axis) was marked at the root collar and breast height positions. After the sample trees were felled, the parameters such as DBH, crown length, and trunk length were measured. Then, a 200 cm-long log was cut from the root upward and transported to the laboratory.

2.2. Experimental Design

2.2.1. Soil and Meteorological Factor Analysis

Twelve equidistant sampling points (1.5 m radius from tree base) were established for soil collection. A 60 cm-deep soil profile was dug at each sampling point. Soil samples were taken at different depths (every 10 cm), and the soil samples of each sampling point were mixed evenly and brought back to the laboratory. The following soil factors were measured in the air-dried samples: pH determination via digital meter (1:2.5 soil–water suspension) (Mettler Toledo, Zurich, Switzerland), soil organic carbon (SOC) by potassium dichromate oxidation (Shenzhen Bolinda Technology Co., Ltd, Shenzhen, China), alkali-hydrolyzable nitrogen (ASN) through micro-Kjeldahl digestion (Haineng Future Technology Group Co., Ltd., Jinan, China), available phosphorus (SAP) using molybdenum–antimony anti-spectrophotometry (Beijing Puxi General Instrument Co., Ltd., Beijing, China), and exchangeable potassium (SAK) measured by flame photometry (Shanghai Metash Instruments Co., Ltd., Shanghai, China).
Meteorological factors including air temperature (T), mean annual precipitation (MAP), relative air humidity (RH), daylight hours (DH), and wind speed (WS) were recorded continuously using automated weather stations installed at both forest farms. Data sourced from compiled materials of the Qiqihar and Daqing Meteorological Bureaus. The influence of meteorological factors on tree growth exhibits a lag effect. The sampled trees, aged 15 years, were analyzed in relation to local meteorological data spanning the 16-year period preceding sample collection. We calculate site-specific monthly average temperatures and precipitation patterns across these 16 years.

2.2.2. Basic Wood Density, Ring Width, and Vessel Characteristics

First, we use a table saw to cut 5 cm-thick disks at the breast height of the intercepted logs (Figure 1). As shown in Figure 1a, according to the Chinese National Standard GB/T 1927.2-2021 [43] for defect-free wood testing, the basic density of wood heartwood, heartwood–sapwood transition parts, and sapwood were calculated according to Formula (1):
ρ = m V
where ρ is the basic density (g/cm3) of the sample, V is the volume (cm3) of the sample when it is saturated with water, and m is the absolute dry mass (g) of the sample. In total, 144 samples were measured in this study.
The cross-sectional wood disks were progressively polished with graded sandpaper (60–1200 grit) to expose distinct annual growth rings. Following hydrothermal softening in a digital thermostatic water bath at 80 °C for 24 h, two perpendicular diameter lines (N-S and E-W axes) were marked through the pith (Figure 1b). Wooden blocks (3 × 3 × 3 cm) were excised along these perpendicular diameter lines and systematically divided into four parts (E, W, N, S). For each part, representative blocks were sectioned into 10–15 μm thin slices using a GSL-1 sliding microtome, subsequently stained with 1% safranin-O aqueous solution (5 min immersion) and dehydrated through a gradient ethanol series (30%–100%). Wood anatomic properties were conducted under an Olympus BX53 biological microscope (Olympus Electronics Inc., Tsukuba, Japan) equipped with a Motic3000 CCD imaging system, obtaining 20-fold magnified images. Quantitative anatomical characterization was performed using ROXAS v1.6, measuring annual ring width (RW), vessel number (CNO), and vessel lumen area (LA).

2.3. Statistical Analysis

Basic wood density, ring width, and vessel characteristics were analyzed by analysis of variance (ANOVA). Correlations between basic wood density, ring width, vessel characteristics, and soil–meteorological factors were analyzed by Pearson’s correlation (p < 0.05 and 0.01). Analyses were performed using IBM SPSS Statistics 20.0 (IBM Corp., Armonk, NY, USA).

3. Results

3.1. Soil and Meteorological Factors

As shown in Table 1, Table 2 and Table 3, significant differences in soil and meteorological factors occurred between the two forest farms. For soil factors, Cuohai forest farm exhibited acidic soils (pH 6.14–7.06) conducive to nutrient availability, while Hongqi’s alkaline conditions (pH 8.05–8.48) suggested potential nutrient immobilization and altered microbial dynamics. Both forest farms showed high SAK and ASN content, with Hongqi demonstrating higher SAP and Cuohai maintaining more stable SOC. For meteorological factors, Hongqi received greater MAP with higher RH compared to Cuohai, though T is almost the same for both forest farms.

3.2. Wood Properties

3.2.1. Basic Wood Density

To observe the radial variation in basic wood density, statistical results from different parts of the disk were plotted as a histogram (Figure 2a), and the average basic wood density was calculated. The average basic wood densities of four poplar clones at the forest farms were listed in a descending order: Clone 20 > Clone 5 > Clone 36 > Clone 11. The corresponding average basic densities for Cuohai forest farm were 0.39 ± 0.01, 0.34 ± 0.02, 0.31 ± 0.01, and 0.30 ± 0.01 g/cm3. The corresponding average basic densities for Hongqi forest farm were 0.41 ± 0.05, 0.39 ± 0.03, 0.29 ± 0.01, and 0.29 ± 0.02 g/cm3. The specimens collected from Cuohai forest farm had a lower variability in basic wood density (CV = 2.04%–5.12%) compared to that from Hongqi forest farm (CV = 4.71%–11.21%). The basic wood density profiles typically increased from pith to bark (e.g., Clone 5 in Cuohai forest farm: 0.33→0.35 g/cm3), except for anomalous sapwood density reduction in Clone 11 in Hongqi forest farm (0.30→0.28 g/cm3).
A multivariate analysis of variance (MANOVA) was conducted on the basic wood density of poplar clones across felled areas and radial parts. Results indicated that poplar clones exerted the most significant influence on basic wood density (F = 93.157, p < 0.001); radial parts significantly affected basic wood density (F = 3.194, p = 0.044), while felling areas had a relatively minor effect, though still approaching statistical significance (F = 3.194, p = 0.076). Additionally, the interaction between felling areas and poplar clones significantly influenced basic wood density (F = 7.153, p < 0.001).

3.2.2. Anatomical Properties

To analyze the impact of meteorological factors on wood properties, we divided the experimental samples into four parts—N, E, W, and S—for testing. Figure 3 shows the RW of each of the four parts of different poplar clones at two forest farms. The poplars Clone 20 and Clone 11 at Cuohai forest farm tended to grow toward the northeast of the tree, while Clone 36 and Clone 5 poplars preferentially grew toward the southwest of the tree, and the corresponding clones in the Hongqi forest farm were basically the opposite. Mean RW values differed substantially between the two forest farms: Clone 20 (2618.24 μm), Clone 5 (2196.42 μm), Clone 36 (1459.29 μm), Clone 11 (1451.5 μm) for Cuohai forest farm; and Clone 5 (1748.78 μm), Clone 36 (1505.50 μm), Clone 20 (1380.78 μm), Clone 11 (1056.79 μm) for Hongqi forest farm.
Figure 4 and Figure 5 show the variation in CNO and LA in the N, S, E, and W districts for the four clones. Most clones exhibited CNO spatial patterns (Figure 1) of S < N and E < W, with inverse LA distributions (e.g., Cuohai Clone 5: CNO S (161.3) < N (441.3), E (232.0 < W407.0); LA S (1438 μm2) > N (615 μm2), E (1441.3 µm2) > W (916.5 µm2)). The comparison of the two forest farms showed generally higher CNO values in Cuohai, except for Clone 36, while LA values were predominantly greater in specimens collected at Hongqi forest farm. The order of average CNO of different clones in the four districts of the forest farm from large to small is Clone 20, Clone 11, Clone 5, Clone 36, and the average LA from large to small is Clone 11, Clone 36, Clone 5, Clone 20; the order of average CNO of different clones in the four districts of the Hongqi forest farm from large to small is Clone 36, Clone 11, Clone 20, Clone 5, and the average LA from large to small is Clone 11, Clone 5, Clone 36, Clone 20.

3.3. Correlation Between Soil and Meteorological Factors and Wood Properties

3.3.1. Correlation Between Soil and Meteorological Factors and Basic Wood Density

Significant correlations between soil–meteorological factors and basic wood density of four poplar clones were revealed through the comprehensive analysis of air-dried soil samples and meteorological records (Figure 6). As a key indicator for evaluating wood quality, basic density plays a decisive role in the selection and breeding of poplar clones. In this study, the mean basic densities of four poplar clones from Cuohai forest farm and Hongqi forest farm ranged from 0.30 to 0.39 g/cm3 and 0.29 to 0.41 g/cm3, respectively, with coefficients of variation (CV) ranging from 2.04% to 5.12% and 4.71% to 11.21%. Clone 20 demonstrated exceptional basic density under alkaline soil factors with elevated SAK levels, showing significant positive correlations with both pH and SAK. Alkaline soil promoted cell wall thickening and enhanced lignin deposition, while its density showed significant positive correlations with RH and MAP, suggesting moisture availability which supports xylem homogenization. Conversely, Clone 5 and Clone 36 thrived in neutral to weak acidic conditions, with Clone 5 exhibiting an inverse relationship between wood density and soil pH along with significant temperature sensitivity, where thermal acceleration of lignification compensated for pH limitations. Clone 36 displayed moderate pH dependence but strong photoperiod responsiveness, indicating daylight-mediated photosynthate allocation to fiber development. SAP and ASN showed minimal influence across all clones, while SOC emerged as a critical factor for Clone 11. The results indicate significant correlations between soil and meteorological factors and the basic wood density of the four poplar clones.

3.3.2. Correlation Between Soil and Meteorological Factors and Anatomical Properties

Figure 7 shows the correlation between soil factors and anatomical properties. The analysis revealed complex relationships between soil factors and xylem development characteristics across poplar clones. Soil pH emerged as a primary driver of radial growth, showing strong positive correlations with average RW across all cultivars, where alkaline conditions particularly enhanced Clone20’s growth through optimized lignification processes. SAK exhibited paradoxical strain-dependent effects—while SAK strongly suppressed RW in Clone 20 and Clone 11, it promoted growth in Clone 5 and Clone 36, suggesting differential potassium allocation between structural reinforcement and cambial activity. SAP created divergent responses, enhancing Clone 20’s vessel LA but constricting Clone 5’s conduit development, potentially through competitive resource allocation between fiber and vessel differentiation. Nitrogen utilization patterns showed particular complexity: ASN positively influenced Clone 36’s conduit number and LA through enhanced cell differentiation, while suppressing CNO in Clone 20 and Clone 5 through possible auxin-mediated inhibition of vessel initiation. SOC functioned as both a structural precursor and a stress buffer, positively correlating with LA across all clones while displaying strain-specific RW relationships—enhancing Clone 36’s growth under alkaline stress but reducing Clone 20’s radial expansion through carbon partitioning trade-offs. The vessel structure of different clones was affected by nutrient distribution, where Clone 20 optimized hydraulic efficiency through SAK-driven lumen enlargement coupled with CNO reduction, contrasting with Clone 36’s nitrogen-dependent conduit proliferation strategy. The inverse SAP and CNO relationships between Clone 5 and Clone11 suggest that phosphorus regulates vessel initiation thresholds through differential phosphatase activation.
Meteorological factors exhibited limited but discernible associations (0.003 ≤ |r| ≤ 0.347). The ring and vessel structure parameters of all clones of poplar were mainly affected by wind speed and sunshine and were least affected by temperature.
The four parts (Figure 1) exhibited distinct growth–hydraulic coordination patterns, reflecting environmental adaptation mechanisms. Southern parts, subjected to intensified solar irradiance, demonstrated reduced CNO coupled with moderate LA enlargement—a strategic balance between maintaining transpiration-driven water flux (via partial conduit optimization) and mitigating embolism risks through hydraulic pathway redundancy [44]. Conversely, northern parts prioritized radial growth (enhanced RW) and hydraulic redundancy (elevated CNO), likely compensating for reduced photosynthetic activity through structural investment. Eastern and western divergences revealed wind-driven adaptations: western sectors showed mechanical reinforcement through high CNO (increased vessel frequency enhancing tissue rigidity), while eastern counterparts optimized LA for efficient water transport, potentially countering prevailing wind-induced xylem tension fluctuations.
For different clones of poplar, Clone 20 grown at Cuohai forest farm demonstrated good growth through high CNO with moderate LA, suggesting specialization in water-abundant conditions. Clone 5 balanced hydraulic efficiency and safety, explaining its cross-site adaptability. At Hongqi forest farm, Clone 5 adopted large-vessel dominance with CNO reduction. Clone 36 shifted toward drought adaptation (CNO increased, LA decreased). Clone 20/11 showed growth inhibition (RW decreased) with LA reductions, indicating salinity sensitivity.
In summary, differences in anatomical properties of poplars reveal the strategy of poplars to adapt to environmental diversity. Among them, Clone 20 showed the strongest growth advantage in most parts, especially in eastern and northern parts. The growth performance of different poplar clones at different zones not only reflects their sensitivity to environmental factors such as light, water, and soil but also reflects the adaptability differences between tree species based on catheter structure and physiological mechanisms which determine the optimal growth in specific environmental conditions. By adjusting the ring area and vessel structure, optimizing the water transport efficiency, and adapting to the environmental conditions in different directions, the importance of environmental orientation to tree growth strategies and the application value of forest management were emphasized. These differential adaptation strategies provide an important ecological basis for forest management and tree species selection and emphasize the key role of considering the directional adaptability of tree species in ecological restoration and stand management.
These interactions delineate two adaptive paradigms: (1) Clone 20’s alkaline-optimized strategy which combines high-SAK lumen expansion with SOC-mediated stress tolerance, favoring single-conduit efficiency under humid conditions (MAP ≥ 1200 mm); (2) acidic-adaptive patterns where Clone 36’s nitrogen-driven conduit proliferation and Clone 5’s phosphorus-sensitive fiber development balance hydraulic safety against growth demands. The hydraulic architecture emerges as a nutrient allocation compromise—potassium and pH regulate lignin deposition for mechanical integrity, while nitrogen–phosphorus–carbon stoichiometry determines conduit quantity–quality trade-offs. High SOC environments (≥2.8%) enable stress decoupling through enhanced carbon partitioning flexibility, particularly evident in Clone 36’s simultaneous CNO elevation (350 vs. 296) and RW maintenance under alkaline stress. This resource optimization matrix explains ecological specialization: Clone 20’s precipitation-driven homogeneous growth versus Clone 5’s drought adaptation through lignification prioritization, mediated through shared potassium signaling pathways but divergent nitrogen–carbon utilization efficiencies.

4. Discussion

Wood serves several functions for tree growth such as transporting and storing water and nutrients and providing mechanical strength to the tree’s body. The formation process of wood determines its physical and anatomical properties, which are significantly influenced by soil and climatic factors [45,46,47]. This study investigated the effects of soil and climatic factors on the wood properties of Clone 5, Clone 11, Clone 20, and Clone 36 across two topographic conditions—the chernozem zone and the saline–alkali zone—in the semi-arid region of the Songnen Plain. The four clones exhibited distinct growth patterns and adaptation strategies in the two areas.
Clone 20 exhibited a significant positive correlation between basic wood density and both pH and SAK, while adopting a strategy characterized by large vessel lumen area but reduced vessel number. Among these, Clone 20 from Hongqi forest farm demonstrated higher basic wood density yet also exhibited substantial standard deviation and coefficient of variation. This indicates that alkaline soil exerted a certain degree of stress on Clone 20, but SAK modulated its adaptation strategy to saline–alkali conditions. Numerous studies demonstrate that adequate potassium maintains K/Na dynamic equilibrium to enhance plant alkalinity tolerance [48,49]. Furthermore, potassium ions improve stomatal conductance and transpiration rates, thereby increasing plant water-use efficiency and, ultimately, enhancing drought resistance [50]. Its positive correlation between basic density and relative humidity and atmospheric pressure further indicates that this strategy relies on adequate water supply to maintain this efficiency-oriented approach while minimizing hydraulic risk. In contrast, Clone 5 and Clone 36 thrive under neutral to slightly acidic conditions, exhibiting an acid adaptation strategy that emphasizes safety or equilibrium. Clone 5’s basic density negatively correlates with pH and exhibits high temperature sensitivity. This mirrors the behavior of most plants, enabling adaptation and optimization of their effective growing season while reducing frost damage risk [51]. Clone 36’s basic density response to light indicates a light-mediated carbon allocation strategy directing photosynthetic products toward fiber and vessel development. Its positive response of vessel number and annual ring width to ASN points to a nitrogen-dependent vessel proliferation strategy that increases hydraulic pathway redundancy. Light and nitrogen play pivotal roles in regulating plant growth and development. Several studies have documented the effects of light intensity and nitrogen availability on wood vessel formation and cell wall thickness [52,53]. The variation in wood properties among Clone 36 clones represents poplar adaptation to shifting nitrogen availability. The differential effects of SAK on radial growth among clones further highlight fundamental differences in potassium allocation between structural reinforcement and cambial activity. Soil and climatic factors influence wood properties such as basic density, annual ring width, tracheary element number, and mean tracheary element pore area by affecting tree carbohydrate metabolism, water transport, phytohormone metabolism, cell wall biosynthesis and modification, and stress responses [54]. The analysis above indicates that Clone 20 demonstrates good adaptability to saline–alkali soils, while Clone 5 is well-suited for cultivation in cold regions.
Anatomical variations across different parts (E, W, N, S) represent the spatial manifestation of trade-offs between hydraulic efficiency and safety. Plant anatomy and morphology not only support their own weight but also maximize light utilization for photosynthesis while resisting external loads (e.g., wind) to maintain mechanical stability. Different plants adapt to wind loads by reducing the length and diameter of vessels, increasing wood density and other mechanisms [55]. In this study, the southern region employs a strategy of moderate vessel lumen area and lower vessel number, reflecting a balance between maintaining transpiration under intense sunshine and managing embolism risk. Conversely, the northern region’s strategy of enhanced radial growth and vessel number represents an investment in hydraulic redundancy and mechanical stability, potentially compensating for a shorter growing season. Differences between eastern and western regions highlight wind as a selective pressure: the west’s high vessel number serves mechanical reinforcement, while the east’s optimized vessel lumen area promotes hydraulic efficiency. These patterns confirm that plasticity in radial growth, vessel number, and vessel lumen area is not random but rather a coordinated, environmentally driven optimization of the hydraulic system.

5. Conclusions

This study investigated the wood property variations of 15-year-old populus cultivars (P. simonii × P. nigra, P.euramericana N3016 × P. ussuriensis, P. alba × P. berolinensis, and P. pseudo-cathayana × P. deltodides) in the black soil (Cuohai forest farm) and saline–alkali soil (Hongqi forest farm) of the Songnen Plain. Research indicates that poplar trees in black soil regions exhibit better wood properties (stable basic wood density and higher average annual ring width). Wood characteristics in both regions are primarily influenced by soil pH, with the basic wood density of P. alba × P. berolinensis showing a positive correlation with soil pH. Additionally, SAK regulation enhances the adaptability of P. simonii × P. nigra and P. alba × P. berolinensis to arid, cold, and saline–alkali environments. Among climatic factors, wind speed and sunshine duration exerted the greatest influence on basic wood density, annual ring characteristics, and vessel structure parameters across all hybrids, while air temperature had the least impact. Consequently, P. simonii × P. nigra and P. alba × P. berolinensis are more suitable for cultivation in the Western Songnen Plain.

Author Contributions

R.Q.: writing—original draft, visualization, methodology, investigation, formal analysis, conceptualization. H.X.: writing—review and editing, supervision, resources, project administration, methodology, funding acquisition, conceptualization. P.W.: writing—review and editing, investigation, formal analysis. T.Z.: writing—review and editing, supervision, conceptualization. Y.H.: writing—review and editing, supervision, conceptualization. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Natural Science Foundation of Heilongjiang Province (Grant number: LH2024C054) and the National Key Research and Development Program of China (Grant number: 2021YFD2201205).

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 01745 g001
Figure 1. Sample selection method.
Figure 1. Sample selection method.
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Figure 2. The density statistics of different poplar clones at two forest farms: (a) Basic wood density; (b) Coefficient of variation.
Figure 2. The density statistics of different poplar clones at two forest farms: (a) Basic wood density; (b) Coefficient of variation.
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Figure 3. Ring width of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
Figure 3. Ring width of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
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Figure 4. Vessel number of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
Figure 4. Vessel number of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
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Figure 5. Vessel lumen area of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
Figure 5. Vessel lumen area of different poplar clones at two forest farms: (a) Cuohai forest farm; (b) Hongqi forest farm.
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Figure 6. Correlation between soil and meteorological factors and basic wood density: (a) Soil factors; (b) Meteorological factors. r indicates the Pearson correlation coefficient, * indicates a significance level of 0.01, and ** indicates a significance level of 0.05.
Figure 6. Correlation between soil and meteorological factors and basic wood density: (a) Soil factors; (b) Meteorological factors. r indicates the Pearson correlation coefficient, * indicates a significance level of 0.01, and ** indicates a significance level of 0.05.
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Figure 7. Correlation between soil factors and anatomical properties: (a) ring width, (b) vessel number, (c) vessel lumen area. * indicates a significance level of 0.01, and ** indicates a significance level of 0.05.
Figure 7. Correlation between soil factors and anatomical properties: (a) ring width, (b) vessel number, (c) vessel lumen area. * indicates a significance level of 0.01, and ** indicates a significance level of 0.05.
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Table 1. Soil factor analysis of Cuohai forest farm in West Heilongjiang.
Table 1. Soil factor analysis of Cuohai forest farm in West Heilongjiang.
Poplar ClonespHSAK (mg/kg)SAP (mg/kg)ASN (mg/kg)SOC (g/100 g)
Clone 116.2274.693.09157.861.96
Clone 367.06211.084.44105.251.79
Clone 56.14125.443.57146.531.81
Clone 206.25205.403.85151.761.96
Table 2. Soil factor analysis of Hongqi forest farm in West Heilongjiang.
Table 2. Soil factor analysis of Hongqi forest farm in West Heilongjiang.
Poplar ClonespHSAK (mg/kg)SAP (mg/kg)ASN (mg/kg)SOC (g/100 g)
Clone 118.23376.594.09111.540.50
Clone 368.43235.494.06120.722.09
Clone 58.05168.985.74109.211.78
Clone 208.48237.585.35122.492.14
Table 3. Meteorological data of Cuohai forest farm and Hongqi forest farm.
Table 3. Meteorological data of Cuohai forest farm and Hongqi forest farm.
Meteorological FactorsCuohai Forest FarmHongqi Forest Farm
MAP (mm)46.35 ± 7.9853.36 ± 9.33
T (°C)4.93 ± 0.34.47 ± 0.54
DH (h)232.21 ± 12.75171.07 ± 44.34
WS (m/s)2.72 ± 0.922.82 ± 0.25
RH (%)57.1 ± 0.5669.83 ± 4.15
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Qin, R.; Xu, H.; Hu, Y.; Wang, P.; Zuo, T. Analysis of Differences in Wood Properties Among Four Poplar Species Under Different Site Conditions. Forests 2025, 16, 1745. https://doi.org/10.3390/f16111745

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Qin R, Xu H, Hu Y, Wang P, Zuo T. Analysis of Differences in Wood Properties Among Four Poplar Species Under Different Site Conditions. Forests. 2025; 16(11):1745. https://doi.org/10.3390/f16111745

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Qin, Ruixia, Huadong Xu, Yanbo Hu, Peng Wang, and Tianshu Zuo. 2025. "Analysis of Differences in Wood Properties Among Four Poplar Species Under Different Site Conditions" Forests 16, no. 11: 1745. https://doi.org/10.3390/f16111745

APA Style

Qin, R., Xu, H., Hu, Y., Wang, P., & Zuo, T. (2025). Analysis of Differences in Wood Properties Among Four Poplar Species Under Different Site Conditions. Forests, 16(11), 1745. https://doi.org/10.3390/f16111745

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